The agricultural industry continuously develops new plant varieties which are designed to produce high yields under a variety of environmental and adverse conditions. At the same time, the industry also seeks to decrease the costs and potential risks associated with traditional approaches such as fertilizers, herbicides and pesticides. In order to meet these demands, plant breeding techniques have been developed and used to produce plants with desirable phenotypes. Such phenotypes may include, for example, increased crop quality and yield, increased crop tolerance to environmental conditions (e.g., drought, extreme temperatures), increased crop tolerance to viruses, fungi, bacteria, and pests, increased crop tolerance to herbicides, and altering the composition of the resulting crop (e.g., increased sugar, starch, protein, or oil).
To breed plants which exhibit a desirable phenotype, a wide variety of ancient (e.g., cross-breeding, hybridization) and modern (e.g., recombinant DNA technology) techniques can be employed. A crucial step in any of these methodologies is the assessment of phenotypes and traits in the altered plants. Although strategies have been developed to reduce the time and expense required for making such assessments, significant time and cost are still necessary to evaluate crops under different stresses, seasons and environmental conditions. As a result, much effort has been made to increase throughput, lower cost and increase the accuracy and precision of evaluating new plant varieties.
One approach to assess new plant varieties is to screen their genomes to determine if they contain genes of interest. This can be accomplished using indirect (e.g., marker assisted selection) or direct detection methods (e.g., southern blots) that determine whether or not a gene of interest is expressed in a plant without having to grow the plant to maturity. However, a drawback of this approach is that it requires knowledge of the particular gene of interest and does not necessarily produce a reliable prediction of the phenotype of the plant at maturity. Other techniques, such as RNA or protein screening, suffer from similar drawbacks, in that genes of interest must be known and that the accuracy and precision of predicting the plant's phenotype are relatively low. As a result, the development of techniques that could accurately predict the development of phenotypes or traits in altered plants, and eliminate the need for growing such plants to maturity under many simulated conditions, would be particularly advantageous.
Metabolomics is the systemic study of the complete set of metabolites (i.e., the metabolome) found in a biological cell, tissue, organ or organism at a given point in time. In plants, metabolomics allows for an unbiased measurement of the metabolite biochemistry that evolves as light energy, water, carbon dioxide and nutrients are converted into biomass within a changing environment. Time scales of this biochemistry range from seconds to months, and variability within the metabolome of an organism may be regulated by alterations in gene expression, stresses or changes in the environment. Although efforts have been made to relate the metabolome of a new plant variety to a phenotype or trait of interest, such studies can be challenging and imprecise.
Traditional metabolome analysis is complex, expensive and time consuming. Typically, the metabolic profiles of altered and unaltered plants (or plant tissues or organs) must be produced. Such plants may need to be grown to maturity under a variety of environmental conditions or under different types of stress. Metabolic profiles usually consist of named metabolites whose identities may or may not be known. High fidelity naming and quantification of metabolites is typically slow and labor intensive. Subsequently, comparisons must be made between the metabolomes of the altered and unaltered plants to determine differences in specific metabolite levels. Amounts of known metabolites among this subgroup are often mapped onto specific metabolic pathways. Finally, predictions can be made to determine what effect, if any, the observed differences may have had on the phenotype or trait of interest. Thus, the use of metabolomics for evaluating and predicting plant phenotypes can be complex and costly.
As such, the development of simple and inexpensive methods that are capable of accurately relating the metabolome to phenotypes or traits in new plant varieties would be extremely beneficial to the agricultural industry. Additionally, methods which could accurately predict the development of such phenotypes or traits early in a plant's life cycle would be particularly advantageous. Furthermore, the development of chemometric models that would eliminate the need to grow new plant varieties under various environmental conditions or under different types of stress in order to predict the development of a phenotype or trait of interest would also be particularly valuable.